Man pages for PLUCR
Policy Learning Under Constraint
| binary_S_p | Constraint function for binary policy |
| check_data | Check input data for validity |
| CVFolds | CVFolds (from SuperLearner package) |
| delta_mu_constant | Constant Conditional Average Treatment Effect estimator for Y |
| delta_mu_linear | Linear-shaped Conditional Average Treatment Effect estimator... |
| delta_mu_mix | Mixed-shape Conditional Average Treatment Effect estimator... |
| delta_mu_null | Null Conditional Average Treatment Effect estimator for Y |
| delta_mu_realistic | Realistic Conditional Average Treatment Effect estimator for... |
| delta_mu_threshold | Thresholded-shaped Conditional Average Treatment Effect... |
| delta_nu_linear | Linear-shaped Conditional Average Treatment Effect estimator... |
| delta_nu_mix | Mixed-shaped Conditional Average Treatment Effect estimator... |
| delta_nu_realistic | Realistic Conditional Average Treatment Effect estimator for... |
| delta_nu_satisfied | Computes the difference in expected outcomes under treatment... |
| delta_nu_threshold | Thresholded Conditional Average Treatment Effect estimator... |
| estimate_mu | Estimate mu |
| estimate_nu | Estimate nu |
| estimate_ps | Estimate propensity score |
| estimate_real_valued_mu | Estimate real-valued mu |
| FW | Frank-Wolfe algorithm |
| generate_data | Synthetic data generator and functions generator |
| generate_realistic_data | Realistic synthetic data generator and functions generator |
| get_opt_beta_lambda | Select Optimal Beta and Lambda Combination |
| grad_Lagrangian_p | Gradient of the objective function |
| HX | Compute the Inverse Propensity Score Weight (IPW) |
| Lagrangian_p | Objective function taking the form of a Lagrangian |
| learn_threshold | Learn Optimal Decision Threshold |
| lwr_upper_bound_estimators | Lower and upper bound estimators for policy value and... |
| main_algorithm | Main algorithm |
| make_psi | Generate psi function |
| model_Xi_linear | Linear treatment effect on Xi Component Function |
| model_Xi_mix | Mixed treatment effect on Xi component function |
| model_Xi_realistic | Realistic treatment effect on Xi Component Function |
| model_Xi_satisfied | Low treatment effect on Xi |
| model_Xi_threshold | Thresholded treatment effect on Xi component function |
| model_Y_constant | Constant treatment effect on Y component function |
| model_Y_linear | Linear treatment effect on Y component function |
| model_Y_mix | Mixed treatment effect on Y component function |
| model_Y_null | No treatment effect on Y component function |
| model_Y_realistic | Realistic treatment effect on Y component function |
| model_Y_threshold | Thresholded treatment effect on Y component function |
| naive_approach_algorithm | Naive approach main algorithm |
| Optimization_Estimation | Iterative optimization procedure |
| oracular_approach_algorithm | Oracular approach main algorithm |
| oracular_process_results | Oracular evaluation of a policy |
| phi | Normalize a Matrix by Column Min-Max Scaling |
| phi_inv | Inverse Min-Max Normalization |
| plot_metric_comparison | Plot metric values for comparison |
| plot_realistic | Plot realistic data setting |
| predict.SL.grf | predict.SL.grf |
| process_results | Evaluate a policy |
| R_p | Risk function for Conditional Average Treatment Effect (CATE) |
| SGD | Stochastic Gradient Descent (SGD) algorithm |
| sigma_beta | Link function |
| sigma_beta_prime | Derivative of link function |
| SL.grf | SL.grf |
| S_p | Constraint function |
| SuperLearner.CV.control | SuperLearner.CV.control (from SuperLearner package) |
| synthetic_data_plot | Plot synthetic data setting |
| update_mu | Update mu via augmented covariate adjustment |
| update_mu_XA | Update mu via augmented covariate adjustment for fixed X |
| update_nu | Update nu via augmented covariate adjustment |
| update_nu_XA | Update nu via augmented covariate adjustment for fixed X |
| visual_treatment_plot | Visualize treatment assignment probability |
| V_p | Oracular approximation of value function |
| V_Pn | Estimation of policy value |
